Five Self-Imposed Roadblocks to Using Data

Data is absolutely essential to the success of a modern higher education institution, but beyond the roadblocks that stand in the way of its implementation the most important thing leaders must be cognizant of is the cleanliness and integrity of their data.

Imagine a diagram filled with thousands of lines intersecting at seventeen random points. Each of those intersections represents completion hurdles for a subset of students. Every one of these junctions has a list of potential solutions to eliminate or lessen that barrier. Identifying and understanding these distinctive challenges requires us to use Big Data.

Public and private entities across the nation are using data science to analyze and understand impediments and find ways to improve their organizations, and higher education is no different. Because of this growing amount of data being collected, colleges and universities are becoming increasingly strategic about the kinds of academic programs and degrees they’re offering. Institutions are adding these programs because every kind of industry knows to grow its business or service behind the informed results provided by data analysis. These results drive strategic initiatives and growth.

Higher education itself has numerous non-profit and governmental agencies that provide trend data that is often discussed but, if that data is negative, it’s often dismissed. While the cornerstone of modern higher education is to use data in research to inform practice, higher education leaders rarely use data to systemically make changes. Over the course of the past 20 years, student information systems have warehoused billions to trillions of pieces of student record information that, when used to analyze meta and micro-trends, can lead to the discovery of any number of system-level changes or strategies that can transform institutional outcomes.

The strategic importance of using data and business analytics for the effective management of postsecondary institutions is essential, but shifting the institutional culture in this direction is not always easy.

Here are some common obstacles institutional leaders face when trying to create a data-driven organization:

1. Only Thinking About the Big Numbers

Institutions often collect and use between 5 and 10 dashboard indicators to demonstrate success. This larger data is helpful to give a broad institutional scorecard. However, in aggregate, many data points taken as a sum are greater than the sum of its parts. For example, a campus with a 70 percent graduation rate may boast this metric with great pride, but it does mean that 30 percent of its students are not graduating. What’s more, data by major will often reveal that some majors may only graduate 40 or 50 percent of its students while others graduate 80 percent of their students. Big Data well analyzed tells the story about the impediments occurring within the majors that are graduating only 40 percent of their students.

To create change, leaders need to understand what is embedded within the numbers and where changes can occur for stronger outcomes.

2. Believing that No One Has the Expertise to Fully Understand the Data

Implementing data-informed change is hard work. While our institutions are filled with data-oriented people, to get to the point of changing requires the analysis of data from multiple lenses, and looking through data analytics to find gaps or glitches that lead leaders to ask deeper questions. Oftentimes, people give the lack of a skilled analyst as a reason why the institution cannot trust or use those data points as drivers for change. Speaking to the previous example, if only 40 percent of your students are successfully persisting and completing on time, there is something wrong. Analytic data that shows where a pattern of behaviors breaks down can identify what the problem may be and the mitigation strategy needed to boost that rate. Typically, however, rather than addressing the issue, some institutional leaders will instead blame the person who provided the data analytics because they are “not qualified to do that type of analysis.”

3. Keeping IT In A Silo

As institutions grapple with how to stay on top of implementing and integrating technology solutions, their IT divisions still live in diverse and sometimes surprising places. On some campuses, IT has become a service provider. On other campuses, IT has become firefighters working to maintain the infrastructures that keep operations running. On other campuses, IT is not a part of the leadership team focused on ensuring clean data is entered and records are well maintained.

At a successful modern institution, IT needs to lead the procurement and implementation process for new technologies and verify that systems are in place to ensure clean data is retained—and that incomplete, inaccurate and duplicate data is removed. Divisional leaders need to understand the implications of leaving fields blank and how that blank field may be an important variable for larger data analysis. IT must be a part of setting norms and expectations for how data is entered, maintained and warehoused.

4. Institutional Will

While the data exists, institutional leaders do not always work with managing leaders or directors to create actionable items that chip away at improvements. Embedded within Big Data are stories, reasons and solutions, but too often leaders lack the will to move forward tackling issues one at a time, even where data shows that big improvements can lead to bigger opportunities.

Leaders say they are data informed, but dismiss poor performing units with the belief that they are unable to make sustainable change. This is simply not true. Big Data can build stronger accountability. Students, at some level, are a commodity or consumers of a good. How students experience that good can be mapped and analyzed to find struggles in pathways. Good leaders need to resolve to use data to create, streamline or modify paths that ensure strong outcomes while at the same time providing the infrastructure and/or services that allow clean data to show how improvements are being made and the resulting enhanced outcomes.

5. Focusing on Short-Term Business Models

Many say higher education is about to see mass transformation thanks to technology. Thanks to technology, we have the ability to model data that produces models showing 3-, 5-, or even 10-year implications from increasing costs and then link trends with these models. While many institutions are trying to create budget models based on some simple assumptions, leaders are not using their data to see the longer term or bigger picture. In a world that is becoming more driven by sound bites or slick strategic plans for marketing, our actions are not integrating data that will ensure long-term institutional sustainability. We think to short term. Many campuses are living year-to-year with a belief that they can’t think big. Simply, they are in survival mode. Data allows them to make changes to see where change needs to occur to move to a longer-term focus. Short-term thinking does not lead to systemic change. It leads to an institution in constant turmoil and constant challenge.

Looking to the Future

For institutions to thrive, getting data systems in place that focus their efforts is critical. Big Data can help to bring significant institutional goals into focus, allowing leaders to understand how they can and will survive highs and lows while building new pathways for the future.

As we add majors and graduate programs that help students enter the workforce with the skills they need to thrive in the labor market of today and tomorrow, leaders need to apply the same principles to higher education. Understanding data and using the analytics to further our missions and enhance our pathways and operations will lead to advancements. However, above all else, we must ensure data integrity so that we can share the diagrams and build intersections of opportunity for long-term sustainability.